Import important libraries¶

In [2]:
import pandas as pd
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport
%matplotlib inline
In [3]:
#Uploading dataset
df=pd.read_csv('CarPrice_data.csv')
In [4]:
#Display Dataset
df
Out[4]:
car_ID symboling CarName fueltype aspiration doornumber carbody drivewheel enginelocation wheelbase ... enginesize fuelsystem boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
0 1 3 alfa-romero giulia gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495.0
1 2 3 alfa-romero stelvio gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 16500.0
2 3 1 alfa-romero Quadrifoglio gas std two hatchback rwd front 94.5 ... 152 mpfi 2.68 3.47 9.0 154 5000 19 26 16500.0
3 4 2 audi 100 ls gas std four sedan fwd front 99.8 ... 109 mpfi 3.19 3.40 10.0 102 5500 24 30 13950.0
4 5 2 audi 100ls gas std four sedan 4wd front 99.4 ... 136 mpfi 3.19 3.40 8.0 115 5500 18 22 17450.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
200 201 -1 volvo 145e (sw) gas std four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 9.5 114 5400 23 28 16845.0
201 202 -1 volvo 144ea gas turbo four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 8.7 160 5300 19 25 19045.0
202 203 -1 volvo 244dl gas std four sedan rwd front 109.1 ... 173 mpfi 3.58 2.87 8.8 134 5500 18 23 21485.0
203 204 -1 volvo 246 diesel turbo four sedan rwd front 109.1 ... 145 idi 3.01 3.40 23.0 106 4800 26 27 22470.0
204 205 -1 volvo 264gl gas turbo four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 9.5 114 5400 19 25 22625.0

205 rows × 26 columns

In [ ]:
In [5]:
#Displaying the first five rows of dataset
df.head()
Out[5]:
car_ID symboling CarName fueltype aspiration doornumber carbody drivewheel enginelocation wheelbase ... enginesize fuelsystem boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
0 1 3 alfa-romero giulia gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 13495.0
1 2 3 alfa-romero stelvio gas std two convertible rwd front 88.6 ... 130 mpfi 3.47 2.68 9.0 111 5000 21 27 16500.0
2 3 1 alfa-romero Quadrifoglio gas std two hatchback rwd front 94.5 ... 152 mpfi 2.68 3.47 9.0 154 5000 19 26 16500.0
3 4 2 audi 100 ls gas std four sedan fwd front 99.8 ... 109 mpfi 3.19 3.40 10.0 102 5500 24 30 13950.0
4 5 2 audi 100ls gas std four sedan 4wd front 99.4 ... 136 mpfi 3.19 3.40 8.0 115 5500 18 22 17450.0

5 rows × 26 columns

In [6]:
#Displaying the last five rows of dataset
df.tail()
Out[6]:
car_ID symboling CarName fueltype aspiration doornumber carbody drivewheel enginelocation wheelbase ... enginesize fuelsystem boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
200 201 -1 volvo 145e (sw) gas std four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 9.5 114 5400 23 28 16845.0
201 202 -1 volvo 144ea gas turbo four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 8.7 160 5300 19 25 19045.0
202 203 -1 volvo 244dl gas std four sedan rwd front 109.1 ... 173 mpfi 3.58 2.87 8.8 134 5500 18 23 21485.0
203 204 -1 volvo 246 diesel turbo four sedan rwd front 109.1 ... 145 idi 3.01 3.40 23.0 106 4800 26 27 22470.0
204 205 -1 volvo 264gl gas turbo four sedan rwd front 109.1 ... 141 mpfi 3.78 3.15 9.5 114 5400 19 25 22625.0

5 rows × 26 columns

In [7]:
#checking for dupliocates in dataset
df.duplicated
Out[7]:
<bound method DataFrame.duplicated of      car_ID  symboling                   CarName fueltype aspiration  \
0         1          3        alfa-romero giulia      gas        std   
1         2          3       alfa-romero stelvio      gas        std   
2         3          1  alfa-romero Quadrifoglio      gas        std   
3         4          2               audi 100 ls      gas        std   
4         5          2                audi 100ls      gas        std   
..      ...        ...                       ...      ...        ...   
200     201         -1           volvo 145e (sw)      gas        std   
201     202         -1               volvo 144ea      gas      turbo   
202     203         -1               volvo 244dl      gas        std   
203     204         -1                 volvo 246   diesel      turbo   
204     205         -1               volvo 264gl      gas      turbo   

    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \
0          two  convertible        rwd          front       88.6  ...   
1          two  convertible        rwd          front       88.6  ...   
2          two    hatchback        rwd          front       94.5  ...   
3         four        sedan        fwd          front       99.8  ...   
4         four        sedan        4wd          front       99.4  ...   
..         ...          ...        ...            ...        ...  ...   
200       four        sedan        rwd          front      109.1  ...   
201       four        sedan        rwd          front      109.1  ...   
202       four        sedan        rwd          front      109.1  ...   
203       four        sedan        rwd          front      109.1  ...   
204       four        sedan        rwd          front      109.1  ...   

     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \
0           130        mpfi       3.47    2.68              9.0        111   
1           130        mpfi       3.47    2.68              9.0        111   
2           152        mpfi       2.68    3.47              9.0        154   
3           109        mpfi       3.19    3.40             10.0        102   
4           136        mpfi       3.19    3.40              8.0        115   
..          ...         ...        ...     ...              ...        ...   
200         141        mpfi       3.78    3.15              9.5        114   
201         141        mpfi       3.78    3.15              8.7        160   
202         173        mpfi       3.58    2.87              8.8        134   
203         145         idi       3.01    3.40             23.0        106   
204         141        mpfi       3.78    3.15              9.5        114   

     peakrpm citympg  highwaympg    price  
0       5000      21          27  13495.0  
1       5000      21          27  16500.0  
2       5000      19          26  16500.0  
3       5500      24          30  13950.0  
4       5500      18          22  17450.0  
..       ...     ...         ...      ...  
200     5400      23          28  16845.0  
201     5300      19          25  19045.0  
202     5500      18          23  21485.0  
203     4800      26          27  22470.0  
204     5400      19          25  22625.0  

[205 rows x 26 columns]>
In [8]:
#exploring dataset description
df.describe()
Out[8]:
car_ID symboling wheelbase carlength carwidth carheight curbweight enginesize boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
count 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000
mean 103.000000 0.834146 98.756585 174.049268 65.907805 53.724878 2555.565854 126.907317 3.329756 3.255415 10.142537 104.117073 5125.121951 25.219512 30.751220 13276.710571
std 59.322565 1.245307 6.021776 12.337289 2.145204 2.443522 520.680204 41.642693 0.270844 0.313597 3.972040 39.544167 476.985643 6.542142 6.886443 7988.852332
min 1.000000 -2.000000 86.600000 141.100000 60.300000 47.800000 1488.000000 61.000000 2.540000 2.070000 7.000000 48.000000 4150.000000 13.000000 16.000000 5118.000000
25% 52.000000 0.000000 94.500000 166.300000 64.100000 52.000000 2145.000000 97.000000 3.150000 3.110000 8.600000 70.000000 4800.000000 19.000000 25.000000 7788.000000
50% 103.000000 1.000000 97.000000 173.200000 65.500000 54.100000 2414.000000 120.000000 3.310000 3.290000 9.000000 95.000000 5200.000000 24.000000 30.000000 10295.000000
75% 154.000000 2.000000 102.400000 183.100000 66.900000 55.500000 2935.000000 141.000000 3.580000 3.410000 9.400000 116.000000 5500.000000 30.000000 34.000000 16503.000000
max 205.000000 3.000000 120.900000 208.100000 72.300000 59.800000 4066.000000 326.000000 3.940000 4.170000 23.000000 288.000000 6600.000000 49.000000 54.000000 45400.000000
In [9]:
df.info
Out[9]:
<bound method DataFrame.info of      car_ID  symboling                   CarName fueltype aspiration  \
0         1          3        alfa-romero giulia      gas        std   
1         2          3       alfa-romero stelvio      gas        std   
2         3          1  alfa-romero Quadrifoglio      gas        std   
3         4          2               audi 100 ls      gas        std   
4         5          2                audi 100ls      gas        std   
..      ...        ...                       ...      ...        ...   
200     201         -1           volvo 145e (sw)      gas        std   
201     202         -1               volvo 144ea      gas      turbo   
202     203         -1               volvo 244dl      gas        std   
203     204         -1                 volvo 246   diesel      turbo   
204     205         -1               volvo 264gl      gas      turbo   

    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \
0          two  convertible        rwd          front       88.6  ...   
1          two  convertible        rwd          front       88.6  ...   
2          two    hatchback        rwd          front       94.5  ...   
3         four        sedan        fwd          front       99.8  ...   
4         four        sedan        4wd          front       99.4  ...   
..         ...          ...        ...            ...        ...  ...   
200       four        sedan        rwd          front      109.1  ...   
201       four        sedan        rwd          front      109.1  ...   
202       four        sedan        rwd          front      109.1  ...   
203       four        sedan        rwd          front      109.1  ...   
204       four        sedan        rwd          front      109.1  ...   

     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \
0           130        mpfi       3.47    2.68              9.0        111   
1           130        mpfi       3.47    2.68              9.0        111   
2           152        mpfi       2.68    3.47              9.0        154   
3           109        mpfi       3.19    3.40             10.0        102   
4           136        mpfi       3.19    3.40              8.0        115   
..          ...         ...        ...     ...              ...        ...   
200         141        mpfi       3.78    3.15              9.5        114   
201         141        mpfi       3.78    3.15              8.7        160   
202         173        mpfi       3.58    2.87              8.8        134   
203         145         idi       3.01    3.40             23.0        106   
204         141        mpfi       3.78    3.15              9.5        114   

     peakrpm citympg  highwaympg    price  
0       5000      21          27  13495.0  
1       5000      21          27  16500.0  
2       5000      19          26  16500.0  
3       5500      24          30  13950.0  
4       5500      18          22  17450.0  
..       ...     ...         ...      ...  
200     5400      23          28  16845.0  
201     5300      19          25  19045.0  
202     5500      18          23  21485.0  
203     4800      26          27  22470.0  
204     5400      19          25  22625.0  

[205 rows x 26 columns]>
In [10]:
#checking for number of duplicates
df.duplicated().sum()
Out[10]:
0
In [104]:
#checking of shape of data
df.shape
Out[104]:
(205, 26)

Dataset has 205 rows and 26 columns

In [11]:
#displaying the 26 columns
df.columns
Out[11]:
Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',
       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',
       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',
       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',
       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',
       'price'],
      dtype='object')
In [12]:
#showing symboling range
df.symboling.unique()
Out[12]:
array([ 3,  1,  2,  0, -1, -2], dtype=int64)
In [13]:
#carnames
df.CarName.unique()
Out[13]:
array(['alfa-romero giulia', 'alfa-romero stelvio',
       'alfa-romero Quadrifoglio', 'audi 100 ls', 'audi 100ls',
       'audi fox', 'audi 5000', 'audi 4000', 'audi 5000s (diesel)',
       'bmw 320i', 'bmw x1', 'bmw x3', 'bmw z4', 'bmw x4', 'bmw x5',
       'chevrolet impala', 'chevrolet monte carlo', 'chevrolet vega 2300',
       'dodge rampage', 'dodge challenger se', 'dodge d200',
       'dodge monaco (sw)', 'dodge colt hardtop', 'dodge colt (sw)',
       'dodge coronet custom', 'dodge dart custom',
       'dodge coronet custom (sw)', 'honda civic', 'honda civic cvcc',
       'honda accord cvcc', 'honda accord lx', 'honda civic 1500 gl',
       'honda accord', 'honda civic 1300', 'honda prelude',
       'honda civic (auto)', 'isuzu MU-X', 'isuzu D-Max ',
       'isuzu D-Max V-Cross', 'jaguar xj', 'jaguar xf', 'jaguar xk',
       'maxda rx3', 'maxda glc deluxe', 'mazda rx2 coupe', 'mazda rx-4',
       'mazda glc deluxe', 'mazda 626', 'mazda glc', 'mazda rx-7 gs',
       'mazda glc 4', 'mazda glc custom l', 'mazda glc custom',
       'buick electra 225 custom', 'buick century luxus (sw)',
       'buick century', 'buick skyhawk', 'buick opel isuzu deluxe',
       'buick skylark', 'buick century special',
       'buick regal sport coupe (turbo)', 'mercury cougar',
       'mitsubishi mirage', 'mitsubishi lancer', 'mitsubishi outlander',
       'mitsubishi g4', 'mitsubishi mirage g4', 'mitsubishi montero',
       'mitsubishi pajero', 'Nissan versa', 'nissan gt-r', 'nissan rogue',
       'nissan latio', 'nissan titan', 'nissan leaf', 'nissan juke',
       'nissan note', 'nissan clipper', 'nissan nv200', 'nissan dayz',
       'nissan fuga', 'nissan otti', 'nissan teana', 'nissan kicks',
       'peugeot 504', 'peugeot 304', 'peugeot 504 (sw)', 'peugeot 604sl',
       'peugeot 505s turbo diesel', 'plymouth fury iii',
       'plymouth cricket', 'plymouth satellite custom (sw)',
       'plymouth fury gran sedan', 'plymouth valiant', 'plymouth duster',
       'porsche macan', 'porcshce panamera', 'porsche cayenne',
       'porsche boxter', 'renault 12tl', 'renault 5 gtl', 'saab 99e',
       'saab 99le', 'saab 99gle', 'subaru', 'subaru dl', 'subaru brz',
       'subaru baja', 'subaru r1', 'subaru r2', 'subaru trezia',
       'subaru tribeca', 'toyota corona mark ii', 'toyota corona',
       'toyota corolla 1200', 'toyota corona hardtop',
       'toyota corolla 1600 (sw)', 'toyota carina', 'toyota mark ii',
       'toyota corolla', 'toyota corolla liftback',
       'toyota celica gt liftback', 'toyota corolla tercel',
       'toyota corona liftback', 'toyota starlet', 'toyota tercel',
       'toyota cressida', 'toyota celica gt', 'toyouta tercel',
       'vokswagen rabbit', 'volkswagen 1131 deluxe sedan',
       'volkswagen model 111', 'volkswagen type 3', 'volkswagen 411 (sw)',
       'volkswagen super beetle', 'volkswagen dasher', 'vw dasher',
       'vw rabbit', 'volkswagen rabbit', 'volkswagen rabbit custom',
       'volvo 145e (sw)', 'volvo 144ea', 'volvo 244dl', 'volvo 245',
       'volvo 264gl', 'volvo diesel', 'volvo 246'], dtype=object)
In [14]:
#engine types
df['enginetype'].unique()
Out[14]:
array(['dohc', 'ohcv', 'ohc', 'l', 'rotor', 'ohcf', 'dohcv'], dtype=object)
In [15]:
#fuelsystem types
df['fuelsystem'].unique()
Out[15]:
array(['mpfi', '2bbl', 'mfi', '1bbl', 'spfi', '4bbl', 'idi', 'spdi'],
      dtype=object)
In [16]:
#fueltypes
df['fueltype'].unique()
Out[16]:
array(['gas', 'diesel'], dtype=object)

fuel is of two types, gas and diesel

Multivariate dataset analysis¶

In [18]:
#pairplot of entire dataset
sns.pairplot(df,palette='coolwarm' )
Out[18]:
<seaborn.axisgrid.PairGrid at 0x186a5650be0>
In [22]:
#dataset correlations
df.corr()
Out[22]:
car_ID symboling wheelbase carlength carwidth carheight curbweight enginesize boreratio stroke compressionratio horsepower peakrpm citympg highwaympg price
car_ID 1.000000 -0.151621 0.129729 0.170636 0.052387 0.255960 0.071962 -0.033930 0.260064 -0.160824 0.150276 -0.015006 -0.203789 0.015940 0.011255 -0.109093
symboling -0.151621 1.000000 -0.531954 -0.357612 -0.232919 -0.541038 -0.227691 -0.105790 -0.130051 -0.008735 -0.178515 0.070873 0.273606 -0.035823 0.034606 -0.079978
wheelbase 0.129729 -0.531954 1.000000 0.874587 0.795144 0.589435 0.776386 0.569329 0.488750 0.160959 0.249786 0.353294 -0.360469 -0.470414 -0.544082 0.577816
carlength 0.170636 -0.357612 0.874587 1.000000 0.841118 0.491029 0.877728 0.683360 0.606454 0.129533 0.158414 0.552623 -0.287242 -0.670909 -0.704662 0.682920
carwidth 0.052387 -0.232919 0.795144 0.841118 1.000000 0.279210 0.867032 0.735433 0.559150 0.182942 0.181129 0.640732 -0.220012 -0.642704 -0.677218 0.759325
carheight 0.255960 -0.541038 0.589435 0.491029 0.279210 1.000000 0.295572 0.067149 0.171071 -0.055307 0.261214 -0.108802 -0.320411 -0.048640 -0.107358 0.119336
curbweight 0.071962 -0.227691 0.776386 0.877728 0.867032 0.295572 1.000000 0.850594 0.648480 0.168790 0.151362 0.750739 -0.266243 -0.757414 -0.797465 0.835305
enginesize -0.033930 -0.105790 0.569329 0.683360 0.735433 0.067149 0.850594 1.000000 0.583774 0.203129 0.028971 0.809769 -0.244660 -0.653658 -0.677470 0.874145
boreratio 0.260064 -0.130051 0.488750 0.606454 0.559150 0.171071 0.648480 0.583774 1.000000 -0.055909 0.005197 0.573677 -0.254976 -0.584532 -0.587012 0.553173
stroke -0.160824 -0.008735 0.160959 0.129533 0.182942 -0.055307 0.168790 0.203129 -0.055909 1.000000 0.186110 0.080940 -0.067964 -0.042145 -0.043931 0.079443
compressionratio 0.150276 -0.178515 0.249786 0.158414 0.181129 0.261214 0.151362 0.028971 0.005197 0.186110 1.000000 -0.204326 -0.435741 0.324701 0.265201 0.067984
horsepower -0.015006 0.070873 0.353294 0.552623 0.640732 -0.108802 0.750739 0.809769 0.573677 0.080940 -0.204326 1.000000 0.131073 -0.801456 -0.770544 0.808139
peakrpm -0.203789 0.273606 -0.360469 -0.287242 -0.220012 -0.320411 -0.266243 -0.244660 -0.254976 -0.067964 -0.435741 0.131073 1.000000 -0.113544 -0.054275 -0.085267
citympg 0.015940 -0.035823 -0.470414 -0.670909 -0.642704 -0.048640 -0.757414 -0.653658 -0.584532 -0.042145 0.324701 -0.801456 -0.113544 1.000000 0.971337 -0.685751
highwaympg 0.011255 0.034606 -0.544082 -0.704662 -0.677218 -0.107358 -0.797465 -0.677470 -0.587012 -0.043931 0.265201 -0.770544 -0.054275 0.971337 1.000000 -0.697599
price -0.109093 -0.079978 0.577816 0.682920 0.759325 0.119336 0.835305 0.874145 0.553173 0.079443 0.067984 0.808139 -0.085267 -0.685751 -0.697599 1.000000
In [23]:
#dataset correlation table
plt.figure(figsize=(20,12))
sns.heatmap(df.corr(), annot=True, cmap="magma",)
plt.show()

Univariate and Bivariate Dataset analysis¶

In [96]:
sns.histplot(x='carheight',data=df, bins=10);
In [93]:
df.isnull().sum()
Out[93]:
car_ID              0
symboling           0
CarName             0
fueltype            0
aspiration          0
doornumber          0
carbody             0
drivewheel          0
enginelocation      0
wheelbase           0
carlength           0
carwidth            0
carheight           0
curbweight          0
enginetype          0
cylindernumber      0
enginesize          0
fuelsystem          0
boreratio           0
stroke              0
compressionratio    0
horsepower          0
peakrpm             0
citympg             0
highwaympg          0
price               0
dtype: int64
In [25]:
abs(df.corr().price).sort_values(ascending=False)
Out[25]:
price               1.000000
enginesize          0.874145
curbweight          0.835305
horsepower          0.808139
carwidth            0.759325
highwaympg          0.697599
citympg             0.685751
carlength           0.682920
wheelbase           0.577816
boreratio           0.553173
carheight           0.119336
car_ID              0.109093
peakrpm             0.085267
symboling           0.079978
stroke              0.079443
compressionratio    0.067984
Name: price, dtype: float64
In [82]:
report=ProfileReport(df)
In [83]:
report

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[83]:
























In [26]:



     


sns.histplot(x='car_ID',data=df, bins=20);



     






















    
    































In [113]:



     


#showing carnames with respect to the ones with highest price
carname_price=df[['CarName','price']].groupby(['CarName']).mean().sort_values(by = 'price',ascending = False)



     



















In [114]:



     


carname_price



     






















    
    
Out[114]:











  
    

      
      price
    

    

      CarName
      
    

  
  
    

      buick regal sport coupe (turbo)
      45400.0
    

    

      bmw x5
      41315.0
    

    

      buick century special
      40960.0
    

    

      porsche boxter
      37028.0
    

    

      jaguar xk
      36000.0
    

    

      ...
      ...
    

    

      Nissan versa
      5499.0
    

    

      mitsubishi mirage
      5389.0
    

    

      toyota corona mark ii
      5348.0
    

    

      maxda rx3
      5195.0
    

    

      chevrolet impala
      5151.0
    

  



147 rows × 1 columns


























In [119]:



     


carname_price.plot(kind='barh',fontsize=1.5)



     






















    
    
Out[119]:








<AxesSubplot:ylabel='CarName'>










    
    































In [28]:



     


plot = sns.distplot(df.price, kde=False,color="b",bins=10)
plt.show()



     






















    
    































In [29]:



     


plt.figure(figsize=(20,8))

plt.subplot(1,2,1)
plt.title('Car Price Distribution Plot')
sns.distplot(df.price)

plt.subplot(1,2,2)
plt.title('Car Price Spread')
sns.boxplot(y=df.price)

plt.show()



     






















    
    































In [89]:



     


plt.figure(figsize=(25, 6))

plt.subplot(1,3,1)
plt1 = df.enginelocation.value_counts().plot(kind='pie')
plt.title('Engine Location')
plt1.set(xlabel = 'enginelocation', ylabel='')

plt.subplot(1,3,2)
plt1 = df.fueltype.value_counts().plot(kind='bar')
plt.title('Fuel Type Histogram')
plt1.set(xlabel = 'Fuel Type', ylabel='Frequency of fuel type')

plt.subplot(1,3,3)
plt1 = df.carbody.value_counts().plot(kind='bar')
plt.title('Car Type Histogram')
plt1.set(xlabel = 'Car Type', ylabel='Frequency of Car type')

plt.show()



     






















    
    































In [31]:



     


sns.FacetGrid(data=df, hue='fueltype').map(sns.histplot,'price').add_legend()



     






















    
    
Out[31]:








<seaborn.axisgrid.FacetGrid at 0x186bfc09400>










    
    































In [11]:



     


plt.figure(figsize=(20,8))

plt.subplot(1,2,1)
plt.title('Symboling Histogram')
sns.countplot(df.symboling, palette=("cubehelix"))

plt.subplot(1,2,2)
plt.title('Symboling vs Price')
sns.boxplot(x=df.symboling, y=df.price, palette=("cubehelix"))

plt.show()



     






















    
    































In [12]:



     


plt.figure(figsize=(20,8))

plt.subplot(1,2,1)
plt.title('Engine Type Histogram')
sns.countplot(df.enginetype, palette=("Blues_d"))

plt.subplot(1,2,2)
plt.title('drivewheel vs Price')
sns.boxplot(x=df.drivewheel, y=df.price, palette=("PuBuGn"))

plt.show()

df = pd.DataFrame(df.groupby(['enginetype'])['price'].mean().sort_values(ascending = False))
df.plot.bar(figsize=(8,6))
plt.title('Engine Type vs Average Price')
plt.show()



     






















    
    

















    
    































In [56]:



     


sns.regplot(x='carwidth',y='carlength',data=df)



     






















    
    
Out[56]:








<AxesSubplot:xlabel='carwidth', ylabel='carlength'>










    
    



































Regression Analysis of Data¶
predicting price¶


















In [57]:



     


#encoding data
for colname in df.select_dtypes("object"):
    df[colname],_=df[colname].factorize()



     



















In [58]:



     


df.columns



     






















    
    
Out[58]:








Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',
       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',
       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',
       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',
       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',
       'price'],
      dtype='object')
























In [59]:



     


x=df.head()



     



















In [60]:



     


x



     






















    
    
Out[60]:











  
    

      
      car_ID
      symboling
      CarName
      fueltype
      aspiration
      doornumber
      carbody
      drivewheel
      enginelocation
      wheelbase
      ...
      enginesize
      fuelsystem
      boreratio
      stroke
      compressionratio
      horsepower
      peakrpm
      citympg
      highwaympg
      price
    

  
  
    

      0
      1
      3
      0
      0
      0
      0
      0
      0
      0
      88.6
      ...
      130
      0
      3.47
      2.68
      9.0
      111
      5000
      21
      27
      13495.0
    

    

      1
      2
      3
      1
      0
      0
      0
      0
      0
      0
      88.6
      ...
      130
      0
      3.47
      2.68
      9.0
      111
      5000
      21
      27
      16500.0
    

    

      2
      3
      1
      2
      0
      0
      0
      1
      0
      0
      94.5
      ...
      152
      0
      2.68
      3.47
      9.0
      154
      5000
      19
      26
      16500.0
    

    

      3
      4
      2
      3
      0
      0
      1
      2
      1
      0
      99.8
      ...
      109
      0
      3.19
      3.40
      10.0
      102
      5500
      24
      30
      13950.0
    

    

      4
      5
      2
      4
      0
      0
      1
      2
      2
      0
      99.4
      ...
      136
      0
      3.19
      3.40
      8.0
      115
      5500
      18
      22
      17450.0
    

  



5 rows × 26 columns


























In [61]:



     


y=df["price"]



     



















In [62]:



     


y.shape



     






















    
    
Out[62]:








(205,)
























In [63]:



     


from sklearn.preprocessing import StandardScaler



     



















In [64]:



     


scaler=StandardScaler()
x=scaler.fit_transform(x)



     



















In [65]:



     


x



     






















    
    
Out[65]:








array([[-1.41421356,  1.06904497, -1.41421356,  0.        ,  0.        ,
        -0.81649658, -1.11803399, -0.75      ,  0.        , -1.13332262,
        -1.01709526, -1.16796938, -1.17596712, -0.36535703, -1.11803399,
        -0.75      , -0.10150259,  0.        ,  0.93560688, -1.221739  ,
         0.        , -0.41745641, -0.81649658,  0.19425717,  0.23284516,
        -1.33448233],
       [-0.70710678,  1.06904497, -0.70710678,  0.        ,  0.        ,
        -0.81649658, -1.11803399, -0.75      ,  0.        , -1.13332262,
        -1.01709526, -1.16796938, -1.17596712, -0.36535703, -1.11803399,
        -0.75      , -0.10150259,  0.        ,  0.93560688, -1.221739  ,
         0.        , -0.41745641, -0.81649658,  0.19425717,  0.23284516,
         0.58975922],
       [ 0.        , -1.60356745,  0.        ,  0.        ,  0.        ,
        -0.81649658,  0.        , -0.75      ,  0.        ,  0.06499341,
        -0.33903175,  0.24164884,  0.27385536,  1.11218977,  0.        ,
         0.5       ,  1.49353809,  0.        , -1.80190954,  0.94232784,
         0.        ,  1.94446801, -0.81649658, -0.77702869, -0.15523011,
         0.58975922],
       [ 0.70710678, -0.26726124,  0.70710678,  0.        ,  0.        ,
         1.22474487,  1.11803399,  0.5       ,  0.        ,  1.1414468 ,
         1.18661113,  0.94645795,  1.03903944, -1.49903839,  1.11803399,
        -0.75      , -1.62404141,  0.        , -0.03465211,  0.75057508,
         1.58113883, -0.91181268,  1.22474487,  1.65118597,  1.39707095,
        -1.04312462],
       [ 1.41421356, -0.26726124,  1.41421356,  0.        ,  0.        ,
         1.22474487,  1.11803399,  1.75      ,  0.        ,  1.06020504,
         1.18661113,  1.14783198,  1.03903944,  1.11756267,  1.11803399,
         1.75      ,  0.3335085 ,  0.        , -0.03465211,  0.75057508,
        -1.58113883, -0.19774251,  1.22474487, -1.26267162, -1.70753116,
         1.1980885 ]])
























In [66]:



     


from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split



     



















In [67]:



     


y=df['price']
x=df.drop('price',axis=1)



     



















In [68]:



     


#splitting data for testing and training
x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.7,random_state=100)



     



















In [69]:



     


x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.7)
print("x train: ",x_train.shape)
print("x test: ",x_test.shape)
print("y train: ",y_train.shape)
print("y test: ",y_test.shape)



     






















    
    






x train:  (61, 25)
x test:  (144, 25)
y train:  (61,)
y test:  (144,)
























In [70]:



     


from sklearn.linear_model import LinearRegression



     



















In [71]:



     


model=LinearRegression()



     



















In [72]:



     


model.fit(x_train, y_train)



     






















    
    
Out[72]:








LinearRegression()
























In [73]:



     


print(model.intercept_)
print(model.coef_)



     






















    
    






15828.592086599467
[ 1.29971594e+02 -3.95879014e+02 -2.05981275e+02 -8.33502310e+03
  4.82603464e+03 -3.45716164e+02  1.78355210e+02 -1.18922965e+03
  1.24582993e+04 -3.07441906e+02  1.95869619e+00  3.49838243e+02
  5.39148335e+00  5.99261635e+00  5.11967905e+02  1.08157878e+03
  6.02468387e+01 -6.62529449e+02 -4.19978223e+03 -1.48786923e+03
  8.64139089e+02 -3.09678262e+00 -5.82035204e-01 -1.22290186e+02
 -6.06443988e+00]
























In [74]:



     


from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error



     



















In [75]:



     


predictions= model.predict(x_test)



     



















In [76]:



     


predictions



     






















    
    
Out[76]:








array([ 8210.59335757,  6429.90029696, 16672.39850189, 13820.89760965,
       10660.58866515,  8062.26147747, 24644.26820409,  6448.41504668,
       13900.10942114,  6173.27916126, 14437.39341575, 11812.94914425,
        8927.38941577, 11969.4365197 , 19292.80066409,  8122.99575365,
       13610.46862364, 22418.42957832,  6674.75780072, 13880.56968568,
       15509.79513071,  5166.54148427,  5586.53138795, 18172.98946991,
        8074.70638331,  8956.38350797,  8301.22827977, 24292.60892378,
       11685.80697562, 10939.52022594, 12150.33119256,  8303.86046338,
       11554.25158369, 17883.34935821, 17908.51949839,  6594.1198265 ,
        5333.23400282, 11092.08540874, 12899.36274708, 14186.62039441,
       18159.26686569, 18527.37390433, 15293.55504999, 11109.79351773,
       19779.97052242,  6299.12229982, 12994.92119868,  7947.57032912,
       10312.78445799, 23332.37961757,  7098.27704401,  6558.87831306,
       21349.35516713,  6653.05840744,  6975.01342117, 16805.96856868,
       18765.0187355 ,  6890.09437828, 15796.55242129, 12922.28976028,
       12518.22676992,  8451.1772727 ,  8308.88947238, 25610.25121   ,
        6258.98860162, 20408.50572441, 17788.34099505,  6946.19331332,
        9285.01436243,  6579.03174758,  6716.33643665, 16561.67167948,
       23226.88969563,  7975.73193302,  6483.01704149, 41910.38793797,
       11848.2879655 , 31831.75547493,  7565.01417218,  9611.37628345,
       12175.19633661,  6925.34362032, 10102.17349654, 17009.49139356,
       12948.8746    ,  8421.1076221 , 33529.41509371,  9066.9677428 ,
       11426.13553755,  8213.51856783, 25534.24152959,  8709.55757911,
        5915.35088738, 13255.78323051, 13392.26260154, 33502.24459929,
        6455.46923664,  5332.95625681, 30326.88377816,  7360.90875177,
       18819.68541253, 25527.15679852, 17017.43658207, 25775.39870917,
       33673.94982177,  9792.63889135,  7547.59963717, 14741.99400954,
        8175.03475877, 16881.97824909, 12022.746769  , 14792.38818046,
        5785.90593263, 21273.76289042, 19845.30702157,  7169.96335023,
       25865.4241543 , 13093.80984987, 19687.83857732,  6545.25275354,
        6279.52619821, 13131.14763565, 23935.48553183, 10617.45591183,
        9097.86773884,  6228.7158998 , 13836.83723985,  8258.14188602,
        7216.00994891,  8289.63419012,  9185.08663876,  7115.53560515,
       12548.83239886, 34104.00968041, 17981.30547876, 16437.49247928,
       18416.87649567,  8028.19211705, 17329.60894414,  8133.13398723,
        5343.80963462,  7396.83473119, 10926.11860514,  5957.11508442])
























In [77]:



     


score=r2_score(predictions,y_test)



     



















In [78]:



     


score



     






















    
    
Out[78]:








0.7437463601459856
























In [79]:



     


print(model.score(x_train,y_train))



     






















    
    






0.9687785281735435
























In [80]:



     


print(model.score(x_test,y_test))



     






















    
    






0.8144749122300077




























OBSERVATIONS¶






















.Car price is affected by engine location, fueltype, door number , carwidth, car length,carwidth drivewheel, enginetype etc
From the data used for this analysis, it is clear that, cars with the front location of engine will have the most patronahe that the ones with their engine at the rear.


.Cars that use diesel as fuel, have low patronage compared to cars that use gas


. The number of doors a car has will also influence its patronage, hence cars with four doors gets the most patronage copared to cars with two doors
. Car with, car length and car heigth is relative with a bit of significance on choice, and capabilities.


. Drivewheel, in relation to price, 'rwd' has the most patronage, followed by 'fwd' then lastly '4wd'.


. Engine type 'dohcv' has the most patronage, followed by 'ohcv' and 'dohc' and other types.



















In [ ]: